- Algorithms and Fast Implementations for Sensing Systems
- Physical Description:
- 1 online resource (150 p.)
- University of Florida
- Place of Publication:
- Gainesville, Fla.
- Publication Date:
- Doctorate ( Ph.D.)
- Degree Grantor:
- University of Florida
- Degree Disciplines:
- Electrical and Computer Engineering
- Committee Chair:
- Li, Jian
- Committee Members:
- Li, Tao
- Subjects / Keywords:
- gmti -- iv -- mimo -- radar -- toeplitz
Electrical and Computer Engineering -- Dissertations, Academic -- UF
- Electrical and Computer Engineering thesis, Ph.D.
bibliography ( marcgt )
theses ( marcgt )
government publication (state, provincial, terriorial, dependent) ( marcgt )
born-digital ( sobekcm )
Electronic Thesis or Dissertation
- In radar signal processing and many of its related areas, one of the most important issues is resolution. Pulse compression radar systems make use of transmit code sequences and receive filters that are specially designed to achieve good range resolution and target detection capability at practically acceptable transmit peak power levels. We show how to design the receive filter (including its length) and the transmit code sequence via data-independent instrumental variables (IV) method, by optimizing a number of relevant metrics considered separately or in combination. Compared with most of the previously published works on the subject, our contribution is more coherent as well as more complete, and yet the approach taken here is generally simpler both conceptually and computationally. We show that, for the negligible Doppler case, IV filters can make the sidelobe levels arbitrarily low. For the nonnegligible Doppler case, the IV performance is unsatisfactory due to the high sidelobe level problems. On the other hand, the data-adaptive approaches result in better performance, but at the cost of implementation complexity.
Many data-adaptive algorithms, such as standard Capon beamformer (SCB) and multiple signal classification (MUSIC) methods, requires multiple snapshots, which is difficult to satisfy in a number of practical applications. We present a nonparametric and hyperparameter free weighted least squares-based iterative adaptive approach for amplitude and phase estimation (IAA-APES, or simply IAA) in array processing. IAA can work well with few snapshots (even one), uncorrelated, partially correlated, and coherent sources, and arbitrary array geometries. IAA is extended to give sparse results via a model-order selection tool, the Bayesian information criterion (BIC). Moreover, it is shown that further improvements in resolution and accuracy can be achieved by applying the parametric RELAX algorithm to refine the IAA\&BIC estimates if desired. We present the IAA in the context of array processing, however, IAA can also be applied to active sensing applications. Simulation results are presented to evaluate the performance of IAA for all these applications, and IAA is shown to outperform a number of existing approaches.
As one application of data-adaptive method, space-time adaptive processing (STAP) has been used in ground moving target indication (GMTI) radar to detect slowly moving targets. We consider applying IAA using primary data only, in conjunction with multiple-input multiple-output (MIMO) transmission schemes, to form high resolution angle-Doppler imaging in GMTI. We compare several MIMO radar transmission schemes, including code division, time division and Doppler frequency division multiplexing approaches, and their conventional single-input multiple-output (SIMO) counterpart. To utilize probing waveforms with low sidelobe levels for range compression, the transmit sequences are designed specifically to have low correlation levels. A simple constant false alarm rate (CFAR) detector is employed to detect targets using the primary data only. To mimic real world scenarios, we apply our algorithms to a simulated dataset which contains high-fidelity, site-specific, simulated ground clutter returns. By combining the usage of intelligent transmission schemes, probing waveforms with good correlation properties, and the adaptive angle-Doppler imaging approach, we show that slow moving targets can be more clearly separated from the clutter ridge in the angle-Doppler images and consequently more easily detected by MIMO radar than by its conventional SIMO counterpart.
Compared with the data independent algorithms, data adaptive methods provide better performance, but at the cost of much higher computational complexity. This problem becomes evident in the STAP or SAR applications where the data size is usually very large. IAA is a robust, user parameter-free and nonparametric adaptive algorithm that can work with a single data sequence or snapshot. Compared to the conventional periodogram, IAA can be used to significantly increase the resolution and suppress the sidelobe levels. However, due to its high computational complexity, IAA can only be used in applications involving small-sized data. Therefore, we consider fast implementations of IAA here for one-dimensional (1-D) and two-dimensional (2-D) spectral estimation of uniformly sampled data. We present herein novel fast implementations of IAA using the Gohberg-Semencul (G-S)-type factorization of the IAA covariance matrices. By exploiting the Toeplitz structure of the said matrices, we are able to reduce the computational cost by at least two orders of magnitudes even for moderate data sizes.
- General Note:
- In the series University of Florida Digital Collections.
- General Note:
- Includes vita.
- Includes bibliographical references.
- Source of Description:
- Description based on online resource; title from PDF title page.
- Source of Description:
- This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
- Statement of Responsibility:
- by Xue Ming.
- Thesis (Ph.D.)--University of Florida, 2011.
- Adviser: Li, Jian.
- Source Institution:
- Rights Management:
- Applicable rights reserved.
- lcc - LD1780 2011
- System ID: